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May 12, 2026BMC Nursing0 citationsOpen Access

Artificial intelligence in nursing education: knowledge, perceptions, and associated factors among Egyptian nursing students across universities

NMNermen Abdelftah MohamedKafrelsheikh UniversitySASamah Osman AliCairo UniversityOROsama Mohamed Elsayed RamadanCairo University

Key Points

  • This research aims to evaluate Egyptian nursing students' knowledge of AI and their perceptions regarding its benefits and fears in healthcare.
  • Cross-sectional descriptive design conducted with a convenience sample of 2412 nursing students.
  • Data collected using a three-part questionnaire covering sociodemographic characteristics, AI knowledge, and attitudes towards AI.
  • Assessment of AI knowledge and perceptions through a dichotomous knowledge test and an attitude scale.
  • 46.4% of students demonstrated good AI knowledge (mean score 3.2 ± 1.3 out of 5).
  • Mean perceived benefits score was 10.9 ± 3.5 out of a maximum of 14, indicating positive views on AI's applications.
  • 72.2% of students feared that AI may replace nurses, highlighting a significant concern regarding professional displacement.

Abstract

AIM: To assess Egyptian nursing students' level of AI knowledge, perceptions of AI benefits, and fears regarding AI in nursing education and practice, and to examine associated sociodemographic factors. BACKGROUND: The rapid integration of artificial intelligence (AI) into healthcare has generated both enthusiasm and apprehension among nursing professionals and students. While AI offers substantial potential benefits in clinical efficiency, decision support, and educational innovation, important concerns remain regarding individualised care, professional displacement, and data privacy. This combination of promise and concern suggests a cautiously receptive context in which nursing students may recognise AI's value while remaining uncertain about its implications for practice and education. Therefore, evidence-based assessment of nursing students' knowledge and perceptions is needed. METHODS: A cross-sectional descriptive design was utilised in this study. A convenience sample of 2412 nursing students was drawn from two nursing faculties between May 2024 and January 2025. Data were collected using a structured three-part questionnaire covering individual sociodemographic characteristics, a dichotomous AI knowledge test, and an attitude scale measuring perceived benefits and fears regarding AI. RESULTS: Approximately 46.4% of students demonstrated good AI knowledge (mean score 3.2 ± 1.3 out of 5). The mean perceived benefits score was 10.9 ± 3.5 (maximum 14), indicating generally favourable views of AI's educational and clinical support applications. The mean fear score was 5.0 ± 1.6. Notably, 72.2% of students expressed concern that AI may replace nurses in the future, and 57.8% reported discomfort with using AI in educational settings. Statistically significant differences were observed across academic years for knowledge, perceived benefits, and fears, and across age groups for perceived benefits and fears; age-related differences in knowledge were smaller but remained statistically significant. No significant differences were found by gender or university. CONCLUSION: Egyptian nursing students demonstrated a foundational understanding of AI and broadly recognised its benefits for education and clinical support. Nevertheless, substantial gaps in formal AI training and pronounced fears about professional displacement were identified. Structured, ethically grounded AI educational programs are urgently needed to enhance students' digital competencies and ensure the safe and effective use of AI in nursing practice. CLINICAL TRIAL REGISTRATION: Not applicable.

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Cite This Study

Mohamed et al. (2026) studied this question.

synapsesocial.com/papers/6a02c2fdce8c8c81e964057fhttps://doi.org/10.1186/s12912-026-04706-9
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